This material is based upon work supported by the National Science Foundation under grants 1318833, 1518833, and 1518861.
Adaptive learning, Neural networks -- Simulations, Chemical systems, Network topologies
Inspired by natural biochemicals that perform complex information processing within living cells, we design and simulate a chemically implemented feedforward neural network, which learns by a novel chemical-reaction-based analogue of backpropagation. Our network is implemented in a simulated chemical system, where individual neurons are separated from each other by semipermeable cell-like membranes. Our compartmentalized, modular design allows a variety of network topologies to be constructed from the same building blocks. This brings us towards general-purpose, adaptive learning in chemico: wet machine learning in an embodied dynamical system.
Blount, D., Banda, P., Teuscher, C., & Stefanovic, D. (2017). Feedforward Chemical Neural Network: An In Silico Chemical System That Learns XOR. Artificial Life. Volume 23, Issue 3, p. 295-317.